Abstract

Personnel rostering problems are highly constrained resource allocation problems. Human rostering experts have many years of experience in making rostering decisions which reflect their individual goals and objectives. We present a novel method for capturing nurse rostering decisions and adapting them to solve new problems using the Case-Based Reasoning (CBR) paradigm. This method stores examples of previously encountered constraint violations and the operations that were used to repair them. The violations are represented as vectors of feature values. We investigate the problem of selecting and weighting features so as to improve the performance of the case-based reasoning approach. A genetic algorithm is developed for off-line feature selection and weighting using the complex data types needed to represent real-world nurse rostering problems. This approach significantly improves the accuracy of the CBR method and reduces the number of features that need to be stored for each problem. The relative importance of different features is also determined, providing an insight into the nature of expert decision making in personnel rostering.

Keywords: Scheduling; Knowledge based systems; Case-based reasoning; Meta-heuristics; Feature selection and weighting

Article Outline

1. Introduction

2. The nurse rostering problem

3. The case-based repair generation method

3.1. Case storage

3.2. Retrieval and adaptation

3.3. Measuring classification accuracy

4. Violation features

4.1. Real-valued features

4.2. Shift and on–off pattern features

4.3. Cover array features

4.4. Features

5. Genetic algorithm for feature weighting and selection

6. Results

7. Conclusion

Acknowledgements

References